The proportion of digital images from ‘unknown’ sources is growing with the increasing use of digital images in the home, office and the internet. Even if the originating equipment provides data in the image file header, meta-data, any subsequent processing could either remove the meta-data or render it useless. The images may be from any digital source such as digital cameras, scanners or camcorders or self generated on the computer.
There are a number of ‘fixing’ algorithms that attempt to correct for poor quality images, such as ‘Auto-levels’ in Adobe Photo-shop. These attempt to improve the appearance of the image on the computer display screen but do not take into account any printer characteristic. U.S. Pat. No. 5,812,286 pins the ‘black ’ and ‘white’ point of each color and uses the median to modify the gamma value but does not relate the changes back to a known printer transfer characteristic. U.S. Pat. No. 5,062,058 uses the cumulative density histogram to set the highlight and shadow points using a display. U.S. Pat. No. 4,984,071 uses the cumulative histogram to generate reference color density values by averaging the shadow and highlight densities for each color. Depending on the method used, the highlight and shadow points are specified with one of the reference color density values.
Computers are increasingly using color management and printer profiles to improve the rendition of printed images, but they cannot take into account the tone characteristics of a digital image that has been through several stages of adjustment.
The invention aims to allow the tone scale of an input digital image from any source to be optimized for the printer being used, the printer transfer characteristic being known, or a processing space, such as ROMM
The definition of the ROMM metric and encoding is described in “Reference Input/Output Medium Metric RGB Color Encodings (RIMM/ROMM RGB), presented by K. E. Spaulding, G. J. Woolfe, E. J. Giorgianni, at PICS 2000 Conference, Mar. 26-29, 2000, Portland Oreg.